Many printed documents contain halftone images that are 1-bit images consisting of dot patterns on a contrasting background. Often the images are composed of black dots printed on light-colored media such as the newsprint of a newspaper. The human eye perceives a grayscale image from the 1-bit, halftone image. While halftone dot patterns reduce the bit-depth of a digital image and maintain the grayscale appearance to a viewer, the characteristics of the quantized image are considerably different than those of a continuous-tone or grayscale image.
When halftone images are scanned or otherwise transformed into digital images, it is often advantageous to process the image to enhance image characteristics or to compress the image to reduce the size of the image file for storage or transmission. Some image processing operations, such as filtering, decimation, interpolation, sharpening, and others, do not work well on halftone images. The high-frequency distribution of dots in halftone images precludes using many image processing methods that function well with grayscale images.
Halftone dot modulation can have deleterious effects when compressing, processing, or reprinting the scanned image. Because many grayscale image processing algorithms and compression methods do not perform well on halftone images, the halftone images must be transformed from halftone to grayscale. This process may be referred to as inverse halftoning or descreening.
Some existing methods for descreening may employ low-pass filtering. However, low-pass filtering that is sufficient to smooth the high-frequency patterns of halftone images will not preserve text and line art edges and other detailed content of the image. It is desirable to maintain edges corresponding to significant image structure. The goal of preservation of image structure precludes the use of simple smoothing techniques.
Accordingly, low-pass filtering methods typically result in grainy or blurred images.
Other existing methods may employ a neural network to transform an image from halftone to grayscale. These methods require training of the neural network and are typically not optimal over a range of halftone techniques. These methods generally do not take advantage of a priori constraints or the nature of the halftone mask, when it is known.
Some current descreening methods involve a wavelet representation that allows selection of useful information from each wavelet band. This may be performed by applying a nonorthogonal, overcomplete, wavelet transform to a halftone image. The high-pass wavelet images are dominated by halftoning blue noise, whose power increases with respect to frequency. Adaptive filtering must then be applied to segregate image detail from halftone modulation. These filters may be adaptive in both space and frequency bands.
Each of the above-described methods has drawbacks related to performance or complexity of the process. It would be advantageous to have a method of descreening that provides superior performance to the more simplistic filtering methods without the complexity of the neural network and wavelet methods.